Fairness in generative modeling

We design general-purpose algorithms for addressing fairness issues There are many facets to fairness. An algorithm may be considered and mode collapse in generative modeling. More precisely, to design to be fair if its results are independent of some variables, particularly fair algorithms for as many sensitive variables as possible, including for sensitive variables. Fairness [18] can be measured in terms of variables we might not be aware of, we assume no prior knowledge separation, i.e., whether the probability of a given prediction, given of sensitive variables: our algorithms use unsupervised fairness the actual value, is the same for all values of a sensitive variable.